Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems - Tran, Dat; Li, Yongce; Clay, Hannah; Golrezaei, Negin; Beygi, Sajjad; Saberi, Amin (Proceedings of the AAAI Symposium Series, 2026)
Users on e-commerce platforms can be uncertain about theirpreferences early in their search. Queries torecommendation systems are frequently ambiguous,incomplete, or weakly specified. Agentic systems areexpected to proactively reason, ask clarifying questions,and act on the user’s behalf, which makes handling suchambiguity increasingly important. In existing platforms,ambiguity led to excessive interactions and questionfatigue or overconfident recommendations prematurelycollapsing the search space.We present an Interactive Decision Support System (IDSS)that addresses ambiguous user queries using entropy as aunifying signal. IDSS maintains a dynamically filteredcandidate product set and quantifies uncertainty over itemattributes using entropy. This uncertainty guides adaptivepreference elicitation by selecting follow-up questionsthat maximize expected information gain. When preferencesremain incomplete, IDSS explicitly incorporates residualuncertainty into downstream recommendations throughuncertainty-aware ranking and entropy-baseddiversification, rather than forcing premature resolution.We evaluate IDSS using review-driven simulated usersgrounded in real user reviews, enabling a controlled studyof diverse shopping behaviors. Our evaluation measures bothinteraction efficiency and recommendation quality. Resultsshow that entropy-guided elicitation reduces unnecessaryfollow-up questions, while uncertainty-aware ranking andpresentation yield more informative, diverse, andtransparent recommendation sets under ambiguous intent.These findings demonstrate that entropy-guided reasoningprovides an effective foundation for agentic recommendationsystems operating under uncertainty.